Concept drift detection and adaptation with hierarchical hypothesis testing

Yu, Shujian; Abraham, Zubin; Wang, Heng; Shah, Mohak; Wei, Yantao; Prı́ncipe, José C.

doi:10.1016/j.jfranklin.2019.01.043

Cited by 44 publications

(22 citation statements)

References 52 publications

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“…This algorithm can handle imbalanced data when the majority and minority classes are not known in advance. Yu et al (2019) implemented Hierarchical Linear Four Rates (HLFR) under the hierarchical hypothesis testing framework. This algorithm minimizes the false positive drift detection by working in two layers.…”

Section: Background and Related Workmentioning

confidence: 99%

“…But this sensitivity of these four rates become the reason for triggering more false-positive detections. Yu et al (2019) tried to minimize the false positive detections using the permutation test. In our proposed hypothesis, instead of reducing the false positive detection, we can reduce the effect of false-positive detection using an ensemble of classifiers.…”

Section: Background and Related Workmentioning

confidence: 99%

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Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Kumar

Singh

Khan

et al. 2021

PeerJ Computer Science

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DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Kumar

Singh

Khan

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…In this section we aim to compare the drift detection ability of HRDD on a wide range of drifts with the latest hierarchical detection methods, HCDT [21] and HLFR [23] introduced in section 1. These consolidated frameworks have already been shown to perform better than their individual base detector counterparts.…”

Section: Experiments 2: Drift Detection Abilitymentioning

confidence: 99%

“…Inspired by this framework, another hierarchical framework named HLFR for supervised data streams is proposed [23]. HLFR incorporates LFR as the base detector in Layer-I and a permutation test in Layer-II.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Reduced-space Drift Detection Framework for Multivariate Supervised Data Streams

Zhang¹,

Tiňo²,

Yao³

2021

IEEE Trans. Knowl. Data Eng.

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In a streaming environment, the characteristics of the data themselves and their relationship with the labels may change over time. Most drift detection methods for supervised data streams are performance-based, that is, they detect changes only after the classification accuracy deteriorates. This may not be sufficient in many application areas where the reason behind a drift is also important. Another category of drift detectors are data distribution-based detectors. Although they can detect some drifts within the input space, changes affecting only the labelling mechanism cannot be identified. Furthermore, little work is available on drift detection for high-dimensional data streams. In this paper we propose an advanced Hierarchical Reduced-space Drift Detection (HRDD) framework for supervised data streams which captures drifts regardless of their effects on classification performance. This framework suggests monitoring both marginal and class-conditional distributions within a lower-dimensional space specifically relevant to the assigned classification task. Experimental comparisons have demonstrated that HRDD not only achieves high-quality performance on high-dimensional data streams, but also outperforms its competitors in terms of detection recall, precision and F-measure across a wide range of different concept drift types including subtle drifts.

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“…For these scenarios, one needs to consider the timeliness of the problems and give higher weights to the new data when necessary, in this way the online model can be better adapted to the new environment. However, detecting and coping with the changes in the distribution of a data stream is a very challenging task [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Online Sequential Extreme Learning Machine With Dynamic Forgetting Factor

Cao

Ming

et al. 2019

IEEE Access

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Online sequential extreme learning machine (OS-ELM) and its variants provide a promising way to solve data stream problems, but most of them do not take the timeliness of the problems into account, which may degrade the performance of the model. The main reason is that these algorithms are unable to adapt to the latest data accordingly when the distribution of the data stream changes. To mitigate this limitation, the forgetting factor is introduced into the relevant models, which is used to balance the relative importance of past data and new data when necessary. However, there is no efficient way to set the forgetting factor properly so far. In this paper, we have developed a novel updating strategy for setting the forgetting factor and proposed a dynamic forgetting factor based OS-ELM algorithm (DOS-ELM). In the sequential learning phase of DOS-ELM, the forgetting factor can be adjusted dynamically according to the change degree of the model accuracy in each learning epoch. This updating process does not require setting any parameters artificially and thus greatly improves the flexibility of the model. The experimental results on ten classification problems, five regression problems, one time-series problem show that DOS-ELM can deal well with both stationary and non-stationary data stream problems. In addition, we have extended DOS-ELM to an online deep model named ML-DOS-ELM, which can handle more complex tasks such as the face recognition problem and the handwritten digit recognition problem. Our experimental evaluations show that both DOS-ELM and ML-DOS-ELM can achieve higher prediction accuracy compared to the other similar algorithms.

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Concept drift detection and adaptation with hierarchical hypothesis testing

Cited by 44 publications

References 52 publications

Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

Hierarchical Reduced-space Drift Detection Framework for Multivariate Supervised Data Streams

Online Sequential Extreme Learning Machine With Dynamic Forgetting Factor

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